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Archive of posts filed under the Causal Inference category.

MRP and Missing Data Question

Andy Timm writes: I’m curious if you have any suggestions for dealing with item nonresponse when using MRP. I haven’t seen anything particularly compelling in a literature review, but it seems like this has to have come up. It seems like a surprisingly large number of papers just go for a complete cases analysis, or […]

“Causal Inference for Social Impact” conference

Jennifer Hill announces this conference:

“Causal Inference: The Mixtape”

A few years ago we reviewed “Mostly Harmless Econometrics,” by Josh Angrist and Jörn-Steffen Pischke. And now we have another friendly introduction to causal inference by an economist, presented as a readable paperback book with a fun title. I’m speaking of “Causal Inference: The Mixtape,” by Scott Cunningham. I like the book—all the blurbs on […]

Postdoc position in Bayesian modeling for cancer

Wesley Tansey writes: I’m recruiting a postdoc to join my lab at Memorial Sloan Kettering Cancer Center (tanseyw@mskcc.org). The role overlaps a lot with the interests of people on your blog. We’re specifically looking for people with experience in subset of the following: – Bayesian hierarchical models – Spatial statistical methods (e.g. Gaussian processes, trend […]

Estimating excess mortality in rural Bangladesh from surveys and MRP

(This post is by Yuling, not by/reviewed by Andrew) Recently I (Yuling) have contributed to a public heath project with many great collaborates: The goal is to understand the excess mortality in potential relevance to Covid-19. Before recent case surge in south Asia, we have seen stories claiming that the pandemic might have hit some low-income […]

“Bayesian Causal Inference for Real World Interactive Systems”

David Rohde points us to this workshop: Machine learning has allowed many systems that we interact with to improve performance and personalize. An important source of information in these systems is to learn from historical actions and their success or failure in applications – which is a type of causal inference. The Bayesian approach is […]

One reason why that estimated effect of Fox News could’ve been so implausibly high.

Ethan Kaplan writes: I just happened upon a post of yours on the potential impact of Fox News on the 2016 election [“No, I don’t buy that claim that Fox news is shifting the vote by 6 percentage points“]. I am one of the authors of the first Fox News study from 2007 (DellaVigna and […]

What’s the biggest mistake revealed by this table? A puzzle:

This came up in our discussion the other day: It’s a table comparing averages for treatment and control groups in an experiment. There’s one big problem here (summarizing differences by p-values) and some little problems, such as reporting values to ridiculous precision (who cares if something has an average of “346.57” when its standard deviation […]

Adjusting for differences between treatment and control groups: “statistical significance” and “multiple testing” have nothing to do with it

Jonathan Falk points us to this post by Scott Alexander entitled “Two Unexpected Multiple Hypothesis Testing Problems.” The important questions, though, have nothing to do with multiple hypothesis testing or with hypothesis testing at all. As is often the case, certain free-floating scientific ideas get in the way of thinking about the real problem. Alexander […]

Many years ago, when he was a baby economist . . .

Jonathan Falk writes: Many years ago, when I was a baby economist, a fight broke out in my firm between two economists. There was a question as to whether a particular change in the telecommunications laws had spurred productivity improvements or not. There a trend of x% per year in productivity improvements that had gone […]

Estimating the college wealth premium: Not so easy

Dale Lehman writes: Here’s the article referenced on Marginal Revolution today. I thought it might be of interest and worth blogging about. It is quite thorough and fairly complex. The results are quite striking – and important. My big concern relates to a critical variable – financial literacy. On page 14 they claim that it […]

Postdoctoral opportunity with Sarah Cowan and Jennifer Hill: causal inference for Universal Basic Income (UBI)

See below from Sarah Cowan: I write to announce the launch of the Cash Transfer Lab. Our mission is to build an evidence base regarding cash transfer policies like a Universal Basic Income. We answer the fundamental questions of how a Universal Basic Income policy would transform American families, communities and economies. The first major […]

PhD student and postdoc positions in Norway for doing Bayesian causal inference using Stan!

Guido Biele writes: I have two positions for a postdoc and PhD student open in a project where we will use observational data from Norwegian National registries, structural models (or the potential outcomes framework, the main thing is that we want to think systematically about identification), and Bayesian estimation in Stan to estimate causal effects […]

Regression discontinuity analysis is often a disaster. So what should you do instead? Here’s my recommendation:

Summary If you have an observational study with outcome y treatment variable z and pre-treatment predictors X, and treatment assignment depends only on X, then you can estimate the average causal effect by regressing y on z and X and looking at the coefficient of z. If there is lack of complete overlap in X […]

Bayesian methods and what they offer compared to classical econometrics

A well-known economist who wishes to remain anonymous writes: Can you write about this agent? He’s getting exponentially big on Twitter. The link is to an econometrician, Jeffrey Wooldridge, who writes: Many useful procedures—shrinkage, for example—can be derived from a Bayesian perspective. But those estimators can be studied from a frequentist perspective, and no strong […]

This one pushes all my buttons

August Wartin writes: Just wanted to make you aware of this ongoing discussion about an article in JPE: It’s the same professor Lidbom that was involved in this discussion a few years ago (I believe you mentioned something about it on your blog). Indeed, we blogged it here. Here’s the abstract of Lidbom’s more recent […]

Statistical fallacies as they arise in political science (from Bob Jervis)

Bob Jervis sends along this fun document he gives to the students in his classes. Enjoy. Theories of International Relations Assume that all the facts and assertions in these paragraphs are correct. Why do the conclusions not follow? (This does not mean that the conclusions are actually false.) What are the alternative explanations for the […]

Drew Bailey on backward causal questions and forward causal inference

Following up on my paper with Guido on backward causal questions and forward causal inference, education researcher Drew Bailey writes: (1) Some disagreements between social scientists or between social scientists and the public arise when one side is in “forward causal inference” mode and the other side is in “backward causal question” mode; (2) Individuals […]

Simulated-data experimentation: Why does it work so well?

Someone sent me a long question about a complicated social science problem involving intermediate outcomes, errors in predictors, latent class analysis, path analysis, and unobserved confounders. I got the gist of the question but it didn’t quite seem worth chasing down all the details involving certain conclusions to be made if certain affects disappeared in […]

The “story time” is to lull us in with a randomized controlled experiment and as we fall asleep, feed us less reliable conclusions that come from an embedded observational study.

Kaiser Fung explains. This comes up a lot, and his formulation in the above title is a good way of putting it. He also has this discussion of the AstraZeneca-Oxford vaccine trial results which makes me want to just do a damn Bayesian analysis of it already. I’ll have to find someone with the right […]

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